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How to parse Spark2.2.0 MLlib

2025-01-16 Update From: SLTechnology News&Howtos shulou NAV: SLTechnology News&Howtos > Internet Technology >

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How to parse Spark2.2.0 MLlib, in view of this problem, this article introduces the corresponding analysis and solution in detail, hoping to help more partners who want to solve this problem to find a more simple and feasible method.

Overview of Spark MLlib

MLlib is the machine learning (Machine Learning) library of Spark, which aims to simplify the engineering practice of machine learning and facilitate expansion to a larger scale.

MLlib consists of some general learning algorithms and tools, including classification, regression, clustering, collaborative filtering, dimension reduction, etc., as well as low-level optimization primitives and high-level pipeline API.

1.Spark MLlib algorithm library

Spark Mllib can provide all types of machine learning algorithms:

MLlib: Main GuideBasic statistics basic Statistics Pipelines Pipeline Extracting Transforming and selecting features feature extraction, Transformation and selection of Classification and Regression Classification and regression Clustering clustering Collaborative filtering Collaborative filtering Frequent Pattern Mining frequent pattern Mining Model selection and tuning Model selection and tuning Advanced topics Advanced topic MLlib: RDD-based API GuideData types data types Basic statistics basic Statistics Classification and regression Classification and regression Collaborative filtering Collaborative filtering Clustering clustering reduced Dimension Feature extraction and transformation feature extraction and Transformation Frequent pattern mining frequent pattern Mining Evaluation metrics Evaluation Index PMML model exportPMML Model Optimization (developer) optimization (developer) the answer to the question on how to parse Spark2.2.0 MLlib is shared here. I hope the above content can help you to a certain extent, if you still have a lot of doubts to be solved, you can follow the industry information channel to learn more related knowledge.

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